to appear at SIGGRAPH 2004
| Abstract: High-quality Monte Carlo image synthesis
requires the ability to importance sample realistic BRDF models. However,
analytic sampling algorithms exist only for the Phong model and its
derivatives such as Lafortune and Blinn-Phong. This paper demonstrates an
importance sampling technique for a wide range of BRDFs, including complex
analytic models such as Cook-Torrance and measured materials, which are
being increasingly used for realistic image synthesis. Our approach is
based on a compact factored representation of the BRDF that is optimized
for sampling. We show that our algorithm consistently offers better
efficiency than alternatives that involve fitting and sampling a Lafortune
or Blinn-Phong lobe, and is more compact than sampling strategies based on
tabulating the full BRDF. We are able to efficiently create images
involving multiple measured and analytic BRDFs, under both complex direct
lighting and global illumination.
jlawrenc@cs.princeton.edu |